改进的BP算法在建筑工程造价估算中的应用

建筑工程造价估算是项目可行性研究阶段的重要内容.研究了工程造价及其影响因素之间复杂的非线性关系,改进了人工神经网络BP算法,在此基础上建立建筑工程造价估算模型.为了验证模型的正确性,收集了莱芜市20个典型建筑工程项目,并选取其中18个作为训练样本,2个作为测试样本,运用MATLAB建模分析,测试结果表明,预测精度与实际值偏差不大,精度满足要求.神经网络在工程造价估算方面具有良好的发展前景.     

建筑工程造价的模糊神经网络确定

研究建筑造价准确估计问题,传统的建筑工程造价估算的方法,存在着各种不同的缺陷,难以获得准确的估价结果。在对影响建筑工程造价特征因素进行分析的基础上,将模糊数学与BP神经网络进行了有机的结合,构造了模糊神经网络的建筑工程造价估算模型,模糊神经网络可从样本数据中提取模糊规则,减少了计算量,将隶属度加入网络模型中,增强了模型的学习能力,有效地提高了估算精度。     

软件开发成本估算技术综述

本文分析了软件开发成本估算精确性的影响因素,并对现有的软件开发成本估算技术进行分类和比较,探讨了软件开发成本估算技术的应用策略及其发展方向。     

电力工程造价的随机权深度神经学习估算方法

为了实现对电力工程造价高效、精确的估算,提出了一种电力工程造价的随机权深度神经学习估算算法（Random Weighted Deep Neural Learning,RWDNL）。通过构建外权随机的带有小中间层的多隐层神经网络模型,利用神经网络深度学习实现了对海量数据有效特征的提取以及电力工程项目造价估算。数值仿真实验结果表明该方法使工程造价估算精度和速度大大提高,可获得令人满意的泛化能力。     

基于BP神经网络的工作量估算模型

为了能够对敏捷开发项目进行有效的工作量估算,提出了一种基于BP神经网络的工作量估算模型。考虑到数据集中存在的一些噪声数据以及错误数据,并且软件开发工作量与其影响因子之间存在着复杂的非线性关系,基于人工神经网络,提出了适合该问题的工作量估算模型;并针对该模型给出了具体的实现和验证,与其他估算方法进行了比较,证实了本文中所提出的工作量估算模型具有良好的性能。     

神经网络在工业厂房造价估算中的应用研究

在工程估价领域的研究中,利用神经网络进行工程造价快速估算,并取得了一定的研究成果。但是真正应用于实际的不多,取得很好效果的更少,神经网络的设计,对训练方法、因素的选择都会影响实际应用效果。为快速提供企业经验数据,根据神经网络原理和工业厂房造价估算的特点,设计了一个基于BP算法的工业厂房造价快速估算神经网络模型,并对样本选取、特征因素确定及处理等关键问题进行了分析。经仿真训练和验证,表明精度能满足要求,最后把训练得到的知识应用于实际,取得了预期的效果。     

自组织特征映射网络在建筑工程分类中的应用

针对传统建筑工程造价估算方法耗时量大、计算繁琐、误差频出的问题,提出了一种用自组织特征映射(SOFM)网络对建筑工程量样本量化后的值进行聚类的方法。该方法不需要手动标识训练数据集就可以实现不同类型的建筑样本自动分类,有助于提高传统建筑工程造价估算的效率。最后,通过实例验证了该方法的实用性和有效性。实验结果表明,改进的方法用于建筑工程造价估算较传统方法而言具有更高的准确率和更低的误报率。     

浅谈软件项目估算技术

由于软件产品自身的特殊性,导致软件项目的估算工作进行困难,估算结果准确性差。为了解决这一问题,产生了很多不同的软件项目估算技术,本文对各种估算技术的主要思想及其优缺点进行简单的阐述。尤其是对功能点估算技术,本文做了详细的介绍,并通过实例加以说明其应用方法。     

有机物性估算系统OPES微机版本的研制（Ⅰ）—物性估算与方法选择

介绍有机物估算系统ＯＰＥＳ微机版本的特点，ＯＰＥＳ内装由系统推荐数据构成的基础物性数据库，并为每种物性均提供多种模型，这些模型反映了物性研究的最新成果，系统采用两种帮助用户选择合适的估算方法：（１）收集整理了模型的适用范围、估算精度、输入参数等参考信息，供用户选择时在线查询；（２）对数据训中的化合物以及估算模型按照极性类别分为四类，自动向用户显示适合于所要计算的化合物的估算模型菜单，缩小了需用户迁     

网络数据传输的响应时间分析与估算

互联网技术的发展,很多应用系统都在通过计算机网络传输数据,但是数据传输网络的响应时间能否满足应用系统的要求?文章提出了一种基于实测数据进行统计概率分析、估算响应时间的方法,对网络数据传输系统的分析、网站建设与维护都是有实际意义的。     

Comparison of estimation methods of cost and duration in IT projects

Producing accurate and reliable project cost estimations at an early stage of a project’s life cycle remains a substantial challenge in the information technology field. This research benchmarks the performance of various approaches to estimating IT project effort and duration. Empirical data were gathered from various “real-world” organizations including several prominent Israeli high-tech companies as well as from the International Software Benchmarking Standards Group (ISBSG) IT project database. The study contrasts two types of models that have been employed to estimate project duration and effort separately: linear regression estimation models and models deriving from a more novel approach based on artificial neural networks (ANNs).     

基于多级神经网络的盾构法隧道施工参数控制

将盾构推进的过程划分成7个阶段，每个阶段用一个神经网络进行模拟，在此基础上构造成多级神经网络，拟合盾构法隧道施工中施工参数与地面沉降之间关系的数学模型。最后根据保证质量，兼顾效益和效率的目标，采用遗传算法进行施工参数的优化匹配，提出相应的控制方案。该方法在多项工程项目中使用，结果表明，此方法对工程实际施工有指导作用。     

Computing confidence intervals for stochastic simulation using neural network metamodels

This paper discusses the use of supervised neural networks as a metamodeling technique for discrete-event, stochastic simulation. An (s, S) inventory simulation from the literature is translated into a metamodel through development of parallel neural networks, one estimating expected total cost and one estimating variance of expected total cost. These neural network estimates are used to form confidence intervals, which are compared for coverage to those formed directly by simulation. It is shown that the neural network metamodel is quite competitive in accuracy when compared to the simulation itself and, once trained, can operate in nearly real-time. A comparison of metamodel performance under interpolative versus extrapolative predictions is made.     

基于Web的神经网络指标预测系统的实现

本文结合指标预测软件的发展现状及趋势,介绍了采用神经网络和主成分分析方法的指标预测系统,根据网络技术的发展情况,重点提出了基于Web的神经网络指标预测系统解决方法。建立了建设工程指标预测的基本模型。系统采用了实际工程项目的资料作为学习内容,运行结果表明,采用该方法建立的建设工程指标预测模型是正确可行的。     

Comparisons between two types of neural networks for manufacturing cost estimation of piping elements

The objective of this paper is to develop and test a model of manufacturing cost estimating of piping elements during the early design phase through the application of artificial neural networks (ANN). The developed model can help designers to make decisions at the early phases of the design process. An ANN model would allow obtaining a fairly accurate prediction, even when enough and adequate information is not available in the early stages of the design process. The developed model is compared with traditional neural networks and conventional regression models. This model proved that neural networks are capable of reducing uncertainties related to the cost estimation of shell and tube heat exchangers.     

云计算对医疗信息化成本的影响研究

积极推进医疗机构的信息化建设是当前医改工作的重要工程,它不仅关系到医疗机构的进步与发展,更关系到人民的身体安康大计。本文概述了当前国内医院信息化建设系统成本方面所面临的实际问题,通过分析笔者所在医院云计算数据中心的论证和建立过程,结合实用效果,阐述了这一问题的信息化建设思路如何通过云计算技术解决。     

Neural networks for cost estimation of shell and tube heat exchangers

The objective of this paper is to develop and test a model of cost estimating for the shell and tube heat exchangers in the early design phase via the application of artificial neural networks (ANN). An ANN model can help the designers to make decisions at the early phases of the design process. With an ANN model, it is possible to obtain a fairly accurate prediction, even when enough and adequate information is not available in the early stages of the design process. This model proved that neural networks are capable of reducing uncertainties related to the cost estimation of a shell and tube heat exchangers.     

基于生物脑网络连接特性的高效神经网络算法

人工神经网络发展至今,已经在计算机视觉、类脑智能等方面得到广泛应用.在过去几十年中,人们对神经网络的研究注重追求更高的准确率,从而忽略了对网络计算成本的控制.而人脑作为高效且节能的网络,其对人工智能的发展起到了重要启示作用.如何仿真生物脑网络的连接特性,建立超低能耗的人工神经网络模型实现基本相同的目标识别正确率成为当前研究的热点.为建立低能耗的人工神经网络模型,本文结合大脑网络的连接特性,通过改变人工神经网络的连接实现网络的高效性.实验结果表明,结合生物脑网络的连接特性,改变网络的连接,很大程度上减少了网络的计算成本,而网络的性能并没有受到明显影响.     

Web-based CBR system applied to early cost budgeting for pavement maintenance project

With the growth of transportation networks in developing countries, the cost-efficacy control of maintenance operations has become critical to the infrastructure asset management after highway construction. To effectively manage numerous projects annually with limited resources, it is necessary to accurately estimate costs and leave a trail of project information during the process of making maintenance project selection decisions. This paper outlines the development of a case-based reasoning (CBR) expert prototype system that compares historical data at the work item-level across the case library. This study attempts to determine preliminary project cost with readily available information rapidly based on previous experience of pavement maintenance related construction to assist decision makers in project screening and budget allocation. Various CBR modeling approaches were presented and assessed in terms of their mean absolute prediction error rates. Design and implementation of a web-based CBR system is demonstrated in this study to efficiently handle the attribute and case similarity computation and the results are displayed using browsers. Furthermore, weighting attributes employed in the CBR system were compared via eigenvector and equal weighting methods for estimating aggregate cost and component costs. Historical generic pavement maintenance projects were gathered from the Taiwan transportation agencies and used for model training and testing. Furthermore, k-fold cross-validation was employed to verify the CBR estimating system. The analytical results demonstrate the ability of the system to estimate the item-level cost of pavement maintenance projects with the satisfactory precision during the conceptual project phase. The developed prototype web-based CBR system can efficiently provide timely and accurate information in an efficient way and provide an alternative estimation tool that can be combined with other evaluation criteria, such as indexes of pavement serviceability and structure strength, to improve the decision making in relation to budget allocation.     

人工智能发展预测

目前,人工智能的推理功能已获突破,学习及联想功能正在研究之中,下一步就是模仿人类右脑的模糊处理功能和整个大脑的并行化处理功能。人工神经网络是未来人工智能应用的新领域,未来智能计算机的构成,可能就是作为主机的冯．诺依曼型机与作为智能外围的人工神经网络的结合。